Ontology-Based Automatic Annotation of Learning Content

doi:10.4018/jswis.2006040103

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Ontology-Based Automatic Annotation of Learning Content

Jelena Jovanovic, Dragan Gasevic, Vladan Devedzic

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)2(2)

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MLA

Jovanovic, Jelena, et al. "Ontology-Based Automatic Annotation of Learning Content." IJSWIS vol.2, no.2 2006: pp.91-119. http://doi.org/10.4018/jswis.2006040103

APA

Jovanovic, J., Gasevic, D., & Devedzic, V. (2006). Ontology-Based Automatic Annotation of Learning Content. International Journal on Semantic Web and Information Systems (IJSWIS), 2(2), 91-119. http://doi.org/10.4018/jswis.2006040103

Chicago

Jovanovic, Jelena, Dragan Gasevic, and Vladan Devedzic. "Ontology-Based Automatic Annotation of Learning Content," International Journal on Semantic Web and Information Systems (IJSWIS) 2, no.2: 91-119. http://doi.org/10.4018/jswis.2006040103

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Abstract

This paper presents an ontology-based approach to automatic annotation of learning objects’ (LOs) content units that we tested in TANGRAM, an integrated learning environment for the domain of Intelligent Information Systems. The approach does not primarily focus on automatic annotation of entire LOs, as other relevant solutions do. Instead, it provides a solution for automatic metadata generation for LOs’ components (i.e., smaller, potentially reusable, content units). Here we mainly report on the content-mining algorithms and heuristics applied for determining values of certain metadata elements used to annotate content units. Specifically, the focus is on the following elements: title, description, unique identifier, subject (based on a domain ontology), and pedagogical role (based on an ontology of pedagogical roles). Additionally, as TANGRAM is grounded on an LO content structure ontology that drives the process of an LO decomposition into its constituent content units, each thus generated content unit is implicitly semantically annotated with its role/position in the LO’s structure. Employing such semantic annotations, TANGRAM allows assembling content units into new LOs personalized to the users’ goals, preferences, and learning styles. In order to provide the evaluation of the proposed solution, we describe our experiences with automatic annotation of slide presentations, one of the most common LO types.

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Ontology-Based Automatic Annotation of Learning Content

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